nw-review-output-format

YAML output format and approval criteria for platform design reviews. Load when generating review feedback.

322 stars

Best use case

nw-review-output-format is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

YAML output format and approval criteria for platform design reviews. Load when generating review feedback.

Teams using nw-review-output-format should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/nw-review-output-format/SKILL.md --create-dirs "https://raw.githubusercontent.com/nWave-ai/nWave/main/nWave/skills/nw-review-output-format/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/nw-review-output-format/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How nw-review-output-format Compares

Feature / Agentnw-review-output-formatStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

YAML output format and approval criteria for platform design reviews. Load when generating review feedback.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Platform Review Output Format

## YAML Structure

```yaml
review_id: "platform_rev_{timestamp}"
reviewer: "nw-platform-architect-reviewer (Atlas)"
artifact_reviewed: "{path to platform design documents}"
review_date: "{ISO 8601 timestamp}"

external_validity_check:
  deployment_path_complete: "{PASS/FAIL}"
  observability_enabled: "{PASS/FAIL}"
  rollback_capability: "{PASS/FAIL}"
  security_gates_present: "{PASS/FAIL}"
  overall_status: "{PASS/FAIL/BLOCKER}"
  blocking_issues:
    - "{issue description if any}"

strengths:
  - category: "{pipeline/infrastructure/deployment/observability/security}"
    description: "{what is done well}"
    evidence: "{specific example from design}"

issues_identified:
  - id: 1
    category: "{pipeline/infrastructure/deployment/observability/security}"
    dimension: "{critique dimension name}"
    severity: "{blocker/critical/high/medium/low}"
    description: "{clear description}"
    impact: "{consequence if not addressed}"
    recommendation: "{specific, actionable fix}"
    evidence: "{where in the design this was found}"

dora_metrics_assessment:
  deployment_frequency_enabled: "{yes/no/partial}"
  lead_time_achievable: "{yes/no/partial}"
  change_failure_rate_trackable: "{yes/no/partial}"
  time_to_restore_measurable: "{yes/no/partial}"
  assessment_notes: "{specific observations}"

priority_validation:
  largest_bottleneck_addressed: "{YES/NO/UNCLEAR}"
  simple_alternatives_documented: "{ADEQUATE/INADEQUATE/MISSING}"
  constraint_prioritization: "{CORRECT/INVERTED/NOT_ANALYZED}"
  verdict: "{PASS/FAIL}"

recommendations:
  immediate:
    - "{must fix before approval}"
  short_term:
    - "{should fix soon}"
  long_term:
    - "{consider for future improvement}"

approval_status: "{approved/rejected_pending_revisions/conditionally_approved}"
conditions_for_approval:
  - "{condition if conditionally_approved}"
```

## Approval Criteria

**Approved**: No blocker or critical issues. High issues acknowledged with timeline.

**Conditionally approved**: No blockers. Critical issues have mitigation plan. High issues documented for follow-up.

**Rejected (pending revisions)**: Any blocker present | multiple critical issues without mitigation | external validity check failed.

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